The present invention relates generally to induction machines and, more particularly, to a system and method for detecting fault in an AC induction machine.
Induction machines are widely used in industry for their cost-efficient and robust nature. Many applications for this “workhorse” of industry are fan and pump industrial applications. For example, in a typical integrated paper mill, low voltage and medium voltage motors may comprise nearly 70% of all driven electrical loads. Due to the prevalence of these motors in industry, it is paramount that the induction motor be reliable. Industry reliability surveys suggest that motor failures typically fall into one of four major categories. Specifically, motor faults typically result from bearing and gearbox failures, stator faults resulting in the opening or shorting of the phase winding, rotor cage failure (e.g., broken rotor bars or end-rings), or air-gap irregularities.
Rotor cage failure is one of the most common failures on medium and high voltage large induction machines. The mechanical performance of the rotor cage decays rapidly due to the high thermal stresses on the rotor cage during motor startup, which can cause thermal expansion and thus mechanical stresses, magnetic stresses caused by electromagnetic forces and unbalanced magnetic pull, dynamic stresses that result from the high rotating torque during operation, environmental stresses due to contamination and abrasion of rotor material, as well as mechanical stresses due to loose laminations, etc. As the mechanical performance of the rotor cage degrades, breakage of the rotor bar or end ring may occur. When rotor cage failure occurs, the performance of the overall motor system largely degrades, often resulting in output torque/speed oscillation, possibly stator/rotor rub, and eventually catastrophic motor system breakdown.
Motor faults not only lead to the repair or replacement of the individual motor, but also cause financial losses due to long lead times for repairs and unexpected process downtime. Furthermore, manually detecting such fault-causing conditions is difficult at best because the motor must be running for detection. As such, an operator usually must remove the motor from operation to perform a maintenance review and diagnosis. However, removing the motor from service is undesirable in many applications because motor down-time can be extremely costly.
In order to avoid such financial losses, detection devices have been designed that generate feedback regarding an operating motor. The feedback is then reviewed by an operator to determine the operating conditions of the motor. However, most systems that monitor operating motors merely provide feedback of faults that have likely already damaged the motor. As such, though operational feedback is sent to the operator, it is often too late for preventive action to be taken.
Some systems have attempted to provide an operator with early fault warning feedback for detecting rotor cage failure using vibration analysis, temperature sensing, and thermal imaging. For example, vibration monitoring has been utilized to provide some early misalignment or unbalance-based faults. However, when a mechanical resonance occurs, machine vibrations are amplified. Due to this amplification, false positives indicating severe mechanical asymmetry are possible. Furthermore, vibration-based monitoring systems typically require highly invasive and specialized monitoring systems to be deployed within the motor system.
In light of the drawbacks of vibration-based monitoring, current-based monitoring techniques have been developed to provide a more inexpensive, non-intrusive technique for detecting faults. For example, the signature frequency component in the stator current spectra has been monitored. If an increase in the signature frequency component is detected, a rotor bar failure is believed to occur. However, the reliability of these techniques is often poor for several reasons. First, the change in the signature frequency component may be caused by reasons unrelated to rotor cage failure. Also, motor failure cannot be detected when no baseline information is available indicating a healthy operation condition.
To improve the reliability of rotor cage failure detection techniques, some methods aim to evaluate fault severity. These methods effectively remove the need to know the initial machine baseline and improve the robustness of the overall fault detection. However, existing fault severity evaluation techniques are only accurate when applied to machines operated under a full load condition. Further, machine monitoring is primarily focused on protection instead of fault prediction.
As faults may develop when a machine is not operating under a full load condition, it would be desirable to design a system and method for detecting rotor cage failure in a machine operating at less than full load at an early stage with high reliability. Further, it would be desirable to design a system and method capable of predicting a rotor fault.